Overview

Dataset statistics

Number of variables16
Number of observations278
Missing cells28
Missing cells (%)0.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory34.9 KiB
Average record size in memory128.5 B

Variable types

DateTime1
Numeric15

Alerts

CNY is highly overall correlated with EUR and 9 other fieldsHigh correlation
Coffee is highly overall correlated with Iron Ore and 9 other fieldsHigh correlation
EUR is highly overall correlated with CNY and 7 other fieldsHigh correlation
Iron Ore is highly overall correlated with Coffee and 9 other fieldsHigh correlation
Meat index is highly overall correlated with CNY and 10 other fieldsHigh correlation
Soybeans is highly overall correlated with Coffee and 9 other fieldsHigh correlation
Sugar is highly overall correlated with Coffee and 9 other fieldsHigh correlation
USD is highly overall correlated with CNY and 7 other fieldsHigh correlation
bud_group_personal_spent_value is highly overall correlated with CNY and 13 other fieldsHigh correlation
bud_type_mandatory_spent_value is highly overall correlated with CNY and 13 other fieldsHigh correlation
eco_GDP_R$_12_months is highly overall correlated with CNY and 13 other fieldsHigh correlation
eco_net_debt_R$ is highly overall correlated with CNY and 13 other fieldsHigh correlation
eco_net_debt_R$_federal_govt is highly overall correlated with CNY and 13 other fieldsHigh correlation
exp_DIC_y is highly overall correlated with CNY and 10 other fieldsHigh correlation
exp_trade_balance_y is highly overall correlated with CNY and 7 other fieldsHigh correlation
eco_net_debt_R$ has 3 (1.1%) missing valuesMissing
eco_net_debt_R$_federal_govt has 3 (1.1%) missing valuesMissing
eco_GDP_R$_12_months has 3 (1.1%) missing valuesMissing
Time has unique valuesUnique

Reproduction

Analysis started2024-02-01 22:06:57.802323
Analysis finished2024-02-01 22:07:33.602799
Duration35.8 seconds
Software versionydata-profiling vv4.6.4
Download configurationconfig.json

Variables

Time
Date

UNIQUE 

Distinct278
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
Minimum2001-01-01 00:00:00
Maximum2024-02-01 00:00:00
2024-02-01T23:07:33.709542image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:33.920039image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

eco_net_debt_R$
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct275
Distinct (%)100.0%
Missing3
Missing (%)1.1%
Infinite0
Infinite (%)0.0%
Mean2316953.8
Minimum541333.74
Maximum6333747.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2024-02-01T23:07:34.124470image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum541333.74
5-th percentile665559.39
Q11061685.4
median1567887.3
Q33550458.9
95-th percentile5552878.9
Maximum6333747.9
Range5792414.2
Interquartile range (IQR)2488773.5

Descriptive statistics

Standard deviation1635211.4
Coefficient of variation (CV)0.70575916
Kurtosis-0.44048864
Mean2316953.8
Median Absolute Deviation (MAD)625662.63
Skewness0.97119847
Sum6.3716229 × 108
Variance2.6739162 × 1012
MonotonicityNot monotonic
2024-02-01T23:07:34.323958image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2423128.13 1
 
0.4%
2805851.53 1
 
0.4%
2742366.38 1
 
0.4%
2672668.3 1
 
0.4%
2627112.1 1
 
0.4%
2493775.18 1
 
0.4%
2458650.72 1
 
0.4%
2360392.03 1
 
0.4%
2033575.55 1
 
0.4%
2286788.39 1
 
0.4%
Other values (265) 265
95.3%
(Missing) 3
 
1.1%
ValueCountFrequency (%)
541333.74 1
0.4%
550253.48 1
0.4%
561959.2 1
0.4%
565464.63 1
0.4%
581727.09 1
0.4%
586060.21 1
0.4%
606367.82 1
0.4%
622953.86 1
0.4%
635685.65 1
0.4%
638367.6 1
0.4%
ValueCountFrequency (%)
6333747.91 1
0.4%
6253007.52 1
0.4%
6211220.8 1
0.4%
6163293.13 1
0.4%
6067062.7 1
0.4%
5992871.68 1
0.4%
5922818.04 1
0.4%
5817539.24 1
0.4%
5794341.14 1
0.4%
5719397.77 1
0.4%

eco_net_debt_R$_federal_govt
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct275
Distinct (%)100.0%
Missing3
Missing (%)1.1%
Infinite0
Infinite (%)0.0%
Mean1655032.4
Minimum337611.99
Maximum5473823.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2024-02-01T23:07:34.517935image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum337611.99
5-th percentile425874.02
Q1708802.82
median1017171.8
Q32549900.9
95-th percentile4619428.7
Maximum5473823.1
Range5136211.1
Interquartile range (IQR)1841098.1

Descriptive statistics

Standard deviation1338846.9
Coefficient of variation (CV)0.8089551
Kurtosis0.39020203
Mean1655032.4
Median Absolute Deviation (MAD)423893.09
Skewness1.245133
Sum4.5513392 × 108
Variance1.7925111 × 1012
MonotonicityNot monotonic
2024-02-01T23:07:34.754297image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1533881.44 1
 
0.4%
1897749.65 1
 
0.4%
1836057.82 1
 
0.4%
1820041.3 1
 
0.4%
1746541.89 1
 
0.4%
1588570.55 1
 
0.4%
1575573.76 1
 
0.4%
1399516.92 1
 
0.4%
1238357.26 1
 
0.4%
1292031.16 1
 
0.4%
Other values (265) 265
95.3%
(Missing) 3
 
1.1%
ValueCountFrequency (%)
337611.99 1
0.4%
346552.5 1
0.4%
355037.5 1
0.4%
360694.52 1
0.4%
374879.99 1
0.4%
378378.89 1
0.4%
395250.93 1
0.4%
405832.79 1
0.4%
408431.08 1
0.4%
411771.96 1
0.4%
ValueCountFrequency (%)
5473823.08 1
0.4%
5402090.79 1
0.4%
5375619.97 1
0.4%
5315415.06 1
0.4%
5256528.02 1
0.4%
5169640.12 1
0.4%
5006344.84 1
0.4%
4915045.88 1
0.4%
4877969.97 1
0.4%
4806136.53 1
0.4%

eco_GDP_R$_12_months
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct275
Distinct (%)100.0%
Missing3
Missing (%)1.1%
Infinite0
Infinite (%)0.0%
Mean4791015.7
Minimum1209046.1
Maximum10803176
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2024-02-01T23:07:34.946733image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1209046.1
5-th percentile1342540
Q12349596.9
median4585738.9
Q36667628.7
95-th percentile9825591.4
Maximum10803176
Range9594130.3
Interquartile range (IQR)4318031.8

Descriptive statistics

Standard deviation2634957.3
Coefficient of variation (CV)0.54997886
Kurtosis-0.79433838
Mean4791015.7
Median Absolute Deviation (MAD)2176289
Skewness0.43317035
Sum1.3175293 × 109
Variance6.9430002 × 1012
MonotonicityNot monotonic
2024-02-01T23:07:35.147688image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6039427.2 1
 
0.4%
6187449.8 1
 
0.4%
6162654 1
 
0.4%
6133275.9 1
 
0.4%
6118507.6 1
 
0.4%
6087368 1
 
0.4%
6063155.5 1
 
0.4%
6026826.4 1
 
0.4%
5907278.6 1
 
0.4%
6003116.9 1
 
0.4%
Other values (265) 265
95.3%
(Missing) 3
 
1.1%
ValueCountFrequency (%)
1209046.1 1
0.4%
1218911 1
0.4%
1234635 1
0.4%
1250830.7 1
0.4%
1263306 1
0.4%
1265570 1
0.4%
1272918.6 1
0.4%
1280805.4 1
0.4%
1289198.6 1
0.4%
1298386.5 1
0.4%
ValueCountFrequency (%)
10803176.4 1
0.4%
10732193.6 1
0.4%
10666257.2 1
0.4%
10620107.5 1
0.4%
10569414.8 1
0.4%
10526477.7 1
0.4%
10476147.9 1
0.4%
10416045.6 1
0.4%
10342854.3 1
0.4%
10245140.9 1
0.4%

Coffee
Real number (ℝ)

HIGH CORRELATION 

Distinct276
Distinct (%)100.0%
Missing2
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean91.62979
Minimum33.181211
Maximum183.39671
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2024-02-01T23:07:35.552371image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum33.181211
5-th percentile37.644156
Q172.800572
median89.413025
Q3109.94264
95-th percentile162.30549
Maximum183.39671
Range150.2155
Interquartile range (IQR)37.142072

Descriptive statistics

Standard deviation35.103615
Coefficient of variation (CV)0.38310265
Kurtosis-0.18050115
Mean91.62979
Median Absolute Deviation (MAD)18.070015
Skewness0.42620432
Sum25289.822
Variance1232.2638
MonotonicityNot monotonic
2024-02-01T23:07:35.778765image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
94.12697354 1
 
0.4%
109.2256194 1
 
0.4%
107.6022162 1
 
0.4%
102.2591064 1
 
0.4%
104.8363193 1
 
0.4%
100.9840626 1
 
0.4%
94.72324215 1
 
0.4%
96.21052189 1
 
0.4%
94.26668466 1
 
0.4%
90.14719033 1
 
0.4%
Other values (266) 266
95.7%
(Missing) 2
 
0.7%
ValueCountFrequency (%)
33.18121093 1
0.4%
34.40419394 1
0.4%
34.47106725 1
0.4%
34.53689171 1
0.4%
35.54873591 1
0.4%
35.56335781 1
0.4%
35.73944425 1
0.4%
35.91780564 1
0.4%
35.97495675 1
0.4%
36.08154634 1
0.4%
ValueCountFrequency (%)
183.3967108 1
0.4%
178.2638053 1
0.4%
177.652549 1
0.4%
176.018388 1
0.4%
170.7968299 1
0.4%
167.8350432 1
0.4%
167.7737306 1
0.4%
167.0920788 1
0.4%
165.4549889 1
0.4%
165.0637573 1
0.4%

Iron Ore
Real number (ℝ)

HIGH CORRELATION 

Distinct189
Distinct (%)68.5%
Missing2
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean135.72877
Minimum21.651086
Maximum368.50523
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2024-02-01T23:07:35.987488image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum21.651086
5-th percentile22.180411
Q157.115839
median122.42527
Q3205.13881
95-th percentile288.1126
Maximum368.50523
Range346.85414
Interquartile range (IQR)148.02297

Descriptive statistics

Standard deviation86.046034
Coefficient of variation (CV)0.63395575
Kurtosis-0.77791813
Mean135.72877
Median Absolute Deviation (MAD)72.164736
Skewness0.38577053
Sum37461.141
Variance7403.92
MonotonicityNot monotonic
2024-02-01T23:07:36.180330image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
22.18041099 12
 
4.3%
21.65108632 12
 
4.3%
23.59763509 12
 
4.3%
27.98590732 12
 
4.3%
47.99779468 12
 
4.3%
57.11583892 12
 
4.3%
62.54568549 12
 
4.3%
103.8159344 11
 
4.0%
123.8091945 1
 
0.4%
114.4800413 1
 
0.4%
Other values (179) 179
64.4%
(Missing) 2
 
0.7%
ValueCountFrequency (%)
21.65108632 12
4.3%
22.18041099 12
4.3%
23.59763509 12
4.3%
27.98590732 12
4.3%
47.99779468 12
4.3%
57.11583892 12
4.3%
62.54568549 12
4.3%
69.80701025 1
 
0.4%
72.06458543 1
 
0.4%
79.374307 1
 
0.4%
ValueCountFrequency (%)
368.5052278 1
0.4%
365.9868711 1
0.4%
346.3783526 1
0.4%
319.6096481 1
0.4%
306.7180313 1
0.4%
306.0881539 1
0.4%
304.3616827 1
0.4%
302.9956836 1
0.4%
302.615377 1
0.4%
302.3895215 1
0.4%

Meat index
Real number (ℝ)

HIGH CORRELATION 

Distinct276
Distinct (%)100.0%
Missing2
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean102.49884
Minimum62.006865
Maximum165.93271
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2024-02-01T23:07:36.387650image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum62.006865
5-th percentile69.172843
Q184.164519
median102.97732
Q3114.27888
95-th percentile145.0024
Maximum165.93271
Range103.92585
Interquartile range (IQR)30.114357

Descriptive statistics

Standard deviation22.475726
Coefficient of variation (CV)0.21927785
Kurtosis-0.025091961
Mean102.49884
Median Absolute Deviation (MAD)15.195288
Skewness0.54219967
Sum28289.681
Variance505.15828
MonotonicityNot monotonic
2024-02-01T23:07:36.596435image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100.5686532 1
 
0.4%
94.38700958 1
 
0.4%
99.38668181 1
 
0.4%
103.1002027 1
 
0.4%
108.8547146 1
 
0.4%
109.4729081 1
 
0.4%
106.1814828 1
 
0.4%
99.6748717 1
 
0.4%
114.8734725 1
 
0.4%
98.4100018 1
 
0.4%
Other values (266) 266
95.7%
(Missing) 2
 
0.7%
ValueCountFrequency (%)
62.00686549 1
0.4%
62.70071092 1
0.4%
64.25407578 1
0.4%
64.57241398 1
0.4%
65.93872914 1
0.4%
66.40308821 1
0.4%
66.50843215 1
0.4%
66.84424609 1
0.4%
66.86704112 1
0.4%
67.67504554 1
0.4%
ValueCountFrequency (%)
165.932715 1
0.4%
165.921585 1
0.4%
165.2829385 1
0.4%
162.8758722 1
0.4%
162.543242 1
0.4%
158.8909804 1
0.4%
153.231688 1
0.4%
151.4055422 1
0.4%
149.2062069 1
0.4%
148.8278397 1
0.4%

Soybeans
Real number (ℝ)

HIGH CORRELATION 

Distinct275
Distinct (%)99.6%
Missing2
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean102.15926
Minimum43.729764
Maximum171.74024
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2024-02-01T23:07:36.793222image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum43.729764
5-th percentile48.616593
Q178.079873
median98.535786
Q3133.60784
95-th percentile153.47634
Maximum171.74024
Range128.01047
Interquartile range (IQR)55.527972

Descriptive statistics

Standard deviation33.869372
Coefficient of variation (CV)0.33153503
Kurtosis-0.96724727
Mean102.15926
Median Absolute Deviation (MAD)31.077455
Skewness0.092702418
Sum28195.955
Variance1147.1344
MonotonicityNot monotonic
2024-02-01T23:07:37.005502image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
57.40718764 2
 
0.7%
89.20552506 1
 
0.4%
90.27119191 1
 
0.4%
87.97250904 1
 
0.4%
89.1395787 1
 
0.4%
89.10898146 1
 
0.4%
88.26122582 1
 
0.4%
101.5622173 1
 
0.4%
97.545026 1
 
0.4%
107.1146486 1
 
0.4%
Other values (265) 265
95.3%
(Missing) 2
 
0.7%
ValueCountFrequency (%)
43.72976372 1
0.4%
44.12868858 1
0.4%
44.15401715 1
0.4%
44.16668143 1
0.4%
44.2869921 1
0.4%
44.81889192 1
0.4%
45.17982394 1
0.4%
45.31913104 1
0.4%
46.16763789 1
0.4%
46.50957349 1
0.4%
ValueCountFrequency (%)
171.7402359 1
0.4%
171.2672997 1
0.4%
170.4237016 1
0.4%
170.1645162 1
0.4%
170.1437448 1
0.4%
169.6084446 1
0.4%
168.0273765 1
0.4%
160.926947 1
0.4%
159.0728147 1
0.4%
159.033174 1
0.4%

Sugar
Real number (ℝ)

HIGH CORRELATION 

Distinct276
Distinct (%)100.0%
Missing2
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean83.535526
Minimum32.829715
Maximum161.86432
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2024-02-01T23:07:37.203824image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum32.829715
5-th percentile39.593097
Q161.154622
median79.96087
Q3103.43688
95-th percentile138.70562
Maximum161.86432
Range129.0346
Interquartile range (IQR)42.282259

Descriptive statistics

Standard deviation30.203956
Coefficient of variation (CV)0.36157019
Kurtosis-0.44989
Mean83.535526
Median Absolute Deviation (MAD)22.353397
Skewness0.43882679
Sum23055.805
Variance912.27894
MonotonicityNot monotonic
2024-02-01T23:07:37.421766image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
84.49101798 1
 
0.4%
124.0038593 1
 
0.4%
116.0327396 1
 
0.4%
109.6329034 1
 
0.4%
107.8418547 1
 
0.4%
105.9130682 1
 
0.4%
92.62755158 1
 
0.4%
86.06779516 1
 
0.4%
67.85186445 1
 
0.4%
75.26326585 1
 
0.4%
Other values (266) 266
95.7%
(Missing) 2
 
0.7%
ValueCountFrequency (%)
32.82971492 1
0.4%
33.25658032 1
0.4%
35.0114891 1
0.4%
35.75838449 1
0.4%
36.24492899 1
0.4%
36.60505209 1
0.4%
36.63393117 1
0.4%
36.70201554 1
0.4%
37.36242521 1
0.4%
37.85865617 1
0.4%
ValueCountFrequency (%)
161.8643182 1
0.4%
159.980352 1
0.4%
159.8990528 1
0.4%
157.5630484 1
0.4%
152.8481404 1
0.4%
151.6590235 1
0.4%
149.4593334 1
0.4%
147.1682084 1
0.4%
146.355285 1
0.4%
146.2259982 1
0.4%

bud_group_personal_spent_value
Real number (ℝ)

HIGH CORRELATION 

Distinct265
Distinct (%)96.0%
Missing2
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean1.9565775 × 1011
Minimum6.2678574 × 1010
Maximum3.6832098 × 1011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2024-02-01T23:07:37.630801image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum6.2678574 × 1010
5-th percentile6.5508889 × 1010
Q11.0503278 × 1011
median1.8738975 × 1011
Q32.8810099 × 1011
95-th percentile3.3631696 × 1011
Maximum3.6832098 × 1011
Range3.0564241 × 1011
Interquartile range (IQR)1.830682 × 1011

Descriptive statistics

Standard deviation9.4157461 × 1010
Coefficient of variation (CV)0.48123553
Kurtosis-1.3389432
Mean1.9565775 × 1011
Median Absolute Deviation (MAD)9.0370515 × 1010
Skewness0.16651718
Sum5.4001539 × 1013
Variance8.8656275 × 1021
MonotonicityIncreasing
2024-02-01T23:07:37.830612image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.26785744 × 101012
 
4.3%
6.473698486 × 10101
 
0.4%
2.652749283 × 10111
 
0.4%
2.403932898 × 10111
 
0.4%
2.420699658 × 10111
 
0.4%
2.437466418 × 10111
 
0.4%
2.454233179 × 10111
 
0.4%
2.470999939 × 10111
 
0.4%
2.487766699 × 10111
 
0.4%
2.504533459 × 10111
 
0.4%
Other values (255) 255
91.7%
(Missing) 2
 
0.7%
ValueCountFrequency (%)
6.26785744 × 101012
4.3%
6.370777963 × 10101
 
0.4%
6.473698486 × 10101
 
0.4%
6.576619009 × 10101
 
0.4%
6.679539532 × 10101
 
0.4%
6.782460055 × 10101
 
0.4%
6.885380578 × 10101
 
0.4%
6.988301101 × 10101
 
0.4%
7.091221624 × 10101
 
0.4%
7.194142147 × 10101
 
0.4%
ValueCountFrequency (%)
3.683209845 × 10111
0.4%
3.65757528 × 10111
0.4%
3.631940715 × 10111
0.4%
3.60630615 × 10111
0.4%
3.580671584 × 10111
0.4%
3.555037019 × 10111
0.4%
3.529402454 × 10111
0.4%
3.503767888 × 10111
0.4%
3.478133323 × 10111
0.4%
3.452498758 × 10111
0.4%

bud_type_mandatory_spent_value
Real number (ℝ)

HIGH CORRELATION 

Distinct265
Distinct (%)96.0%
Missing2
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean9.4939435 × 1011
Minimum1.363717 × 1011
Maximum2.3830425 × 1012
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2024-02-01T23:07:38.024994image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1.363717 × 1011
5-th percentile1.6316194 × 1011
Q14.3560126 × 1011
median8.4314427 × 1011
Q31.3983507 × 1012
95-th percentile2.0450727 × 1012
Maximum2.3830425 × 1012
Range2.2466708 × 1012
Interquartile range (IQR)9.627494 × 1011

Descriptive statistics

Standard deviation5.9064281 × 1011
Coefficient of variation (CV)0.6221259
Kurtosis-0.76282153
Mean9.4939435 × 1011
Median Absolute Deviation (MAD)4.7392245 × 1011
Skewness0.49562009
Sum2.6203284 × 1014
Variance3.4885893 × 1023
MonotonicityIncreasing
2024-02-01T23:07:38.219452image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.363716967 × 101112
 
4.3%
1.558555075 × 10111
 
0.4%
1.329283297 × 10121
 
0.4%
1.210204345 × 10121
 
0.4%
1.219582968 × 10121
 
0.4%
1.22896159 × 10121
 
0.4%
1.238340212 × 10121
 
0.4%
1.247718834 × 10121
 
0.4%
1.257097456 × 10121
 
0.4%
1.266476079 × 10121
 
0.4%
Other values (255) 255
91.7%
(Missing) 2
 
0.7%
ValueCountFrequency (%)
1.363716967 × 101112
4.3%
1.461136021 × 10111
 
0.4%
1.558555075 × 10111
 
0.4%
1.655974129 × 10111
 
0.4%
1.753393183 × 10111
 
0.4%
1.850812237 × 10111
 
0.4%
1.948231291 × 10111
 
0.4%
2.045650345 × 10111
 
0.4%
2.143069399 × 10111
 
0.4%
2.240488453 × 10111
 
0.4%
ValueCountFrequency (%)
2.383042464 × 10121
0.4%
2.358983034 × 10121
0.4%
2.334923603 × 10121
0.4%
2.310864173 × 10121
0.4%
2.286804743 × 10121
0.4%
2.262745313 × 10121
0.4%
2.238685882 × 10121
0.4%
2.214626452 × 10121
0.4%
2.190567022 × 10121
0.4%
2.166507592 × 10121
0.4%

exp_DIC_y
Real number (ℝ)

HIGH CORRELATION 

Distinct106
Distinct (%)38.3%
Missing1
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean45.44769
Minimum8.3
Maximum85
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2024-02-01T23:07:38.415946image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum8.3
5-th percentile12.08
Q119.8
median55
Q363
95-th percentile80
Maximum85
Range76.7
Interquartile range (IQR)43.2

Descriptive statistics

Standard deviation23.78155
Coefficient of variation (CV)0.52327303
Kurtosis-1.4298568
Mean45.44769
Median Absolute Deviation (MAD)21.13
Skewness-0.073439701
Sum12589.01
Variance565.56213
MonotonicityNot monotonic
2024-02-01T23:07:38.619478image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60 35
 
12.6%
80 19
 
6.8%
55 16
 
5.8%
75 11
 
4.0%
16 10
 
3.6%
25 8
 
2.9%
65 6
 
2.2%
30 6
 
2.2%
35 6
 
2.2%
15 6
 
2.2%
Other values (96) 154
55.4%
ValueCountFrequency (%)
8.3 1
 
0.4%
8.65 1
 
0.4%
9 3
1.1%
9.4 1
 
0.4%
10 3
1.1%
10.26 1
 
0.4%
11.5 1
 
0.4%
12 3
1.1%
12.1 1
 
0.4%
12.6 1
 
0.4%
ValueCountFrequency (%)
85 3
 
1.1%
83.2 1
 
0.4%
82.65 1
 
0.4%
82 1
 
0.4%
81.89 1
 
0.4%
81.6 1
 
0.4%
80 19
6.8%
79.5 2
 
0.7%
78.57 1
 
0.4%
78 1
 
0.4%

exp_trade_balance_y
Real number (ℝ)

HIGH CORRELATION 

Distinct197
Distinct (%)71.1%
Missing1
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean32.477168
Minimum-2
Maximum81.3
Zeros2
Zeros (%)0.7%
Negative8
Negative (%)2.9%
Memory size2.3 KiB
2024-02-01T23:07:38.823504image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile1.54
Q115
median29.1
Q351.1
95-th percentile68.1664
Maximum81.3
Range83.3
Interquartile range (IQR)36.1

Descriptive statistics

Standard deviation21.766712
Coefficient of variation (CV)0.67021584
Kurtosis-1.1264242
Mean32.477168
Median Absolute Deviation (MAD)18
Skewness0.18942087
Sum8996.1756
Variance473.78977
MonotonicityNot monotonic
2024-02-01T23:07:39.016893image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40 9
 
3.2%
55 8
 
2.9%
20 6
 
2.2%
24 6
 
2.2%
15 4
 
1.4%
42 4
 
1.4%
10 4
 
1.4%
25 4
 
1.4%
3 4
 
1.4%
16 3
 
1.1%
Other values (187) 225
80.9%
ValueCountFrequency (%)
-2 1
0.4%
-1.5 1
0.4%
-1.32 1
0.4%
-1.25 1
0.4%
-1 2
0.7%
-0.8 1
0.4%
-0.27 1
0.4%
0 2
0.7%
0.5 1
0.4%
1.2 2
0.7%
ValueCountFrequency (%)
81.3 1
0.4%
78.45 1
0.4%
78.4 1
0.4%
75.15 1
0.4%
73 1
0.4%
72.1 1
0.4%
70.85 1
0.4%
70.5 1
0.4%
70.4 1
0.4%
70.37 1
0.4%

CNY
Real number (ℝ)

HIGH CORRELATION 

Distinct275
Distinct (%)99.3%
Missing1
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean0.4375782
Minimum0.22932
Maximum0.8831
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2024-02-01T23:07:39.208144image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0.22932
5-th percentile0.2448648
Q10.280918
median0.359021
Q30.5587
95-th percentile0.80136
Maximum0.8831
Range0.65378
Interquartile range (IQR)0.277782

Descriptive statistics

Standard deviation0.18513437
Coefficient of variation (CV)0.42308864
Kurtosis-0.47772809
Mean0.4375782
Median Absolute Deviation (MAD)0.101279
Skewness0.86209337
Sum121.20916
Variance0.034274734
MonotonicityNot monotonic
2024-02-01T23:07:39.418176image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.5519 2
 
0.7%
0.488 2
 
0.7%
0.238588 1
 
0.4%
0.4868 1
 
0.4%
0.4852 1
 
0.4%
0.483 1
 
0.4%
0.5464 1
 
0.4%
0.533 1
 
0.4%
0.6073 1
 
0.4%
0.5004 1
 
0.4%
Other values (265) 265
95.3%
ValueCountFrequency (%)
0.22932 1
0.4%
0.232245 1
0.4%
0.233655 1
0.4%
0.234784 1
0.4%
0.236701 1
0.4%
0.238588 1
0.4%
0.239 1
0.4%
0.241477 1
0.4%
0.2415 1
0.4%
0.24153 1
0.4%
ValueCountFrequency (%)
0.8831 1
0.4%
0.881 1
0.4%
0.8787 1
0.4%
0.8696 1
0.4%
0.8625 1
0.4%
0.8543 1
0.4%
0.8523 1
0.4%
0.8439 1
0.4%
0.8423 1
0.4%
0.8348 1
0.4%

EUR
Real number (ℝ)

HIGH CORRELATION 

Distinct276
Distinct (%)99.6%
Missing1
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean3.5833491
Minimum1.8437
Maximum6.7241
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2024-02-01T23:07:39.619372image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1.8437
5-th percentile2.22762
Q12.62367
median3.2632
Q34.2578
95-th percentile6.18278
Maximum6.7241
Range4.8804
Interquartile range (IQR)1.63413

Descriptive statistics

Standard deviation1.2298833
Coefficient of variation (CV)0.34322174
Kurtosis-0.060264547
Mean3.5833491
Median Absolute Deviation (MAD)0.6937
Skewness0.93196644
Sum992.5877
Variance1.512613
MonotonicityNot monotonic
2024-02-01T23:07:39.816262image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.5244 2
 
0.7%
1.8437 1
 
0.4%
3.6002 1
 
0.4%
3.6484 1
 
0.4%
3.6116 1
 
0.4%
3.6183 1
 
0.4%
3.5414 1
 
0.4%
4.0039 1
 
0.4%
3.9484 1
 
0.4%
4.0539 1
 
0.4%
Other values (266) 266
95.7%
ValueCountFrequency (%)
1.8437 1
0.4%
1.89153 1
0.4%
1.90165 1
0.4%
1.94164 1
0.4%
1.9636 1
0.4%
2.00134 1
0.4%
2.02603 1
0.4%
2.04419 1
0.4%
2.06363 1
0.4%
2.08247 1
0.4%
ValueCountFrequency (%)
6.7241 1
0.4%
6.7142 1
0.4%
6.6915 1
0.4%
6.6532 1
0.4%
6.6132 1
0.4%
6.5393 1
0.4%
6.5194 1
0.4%
6.5016 1
0.4%
6.4 1
0.4%
6.3799 1
0.4%

USD
Real number (ℝ)

HIGH CORRELATION 

Distinct276
Distinct (%)99.6%
Missing1
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean3.0513617
Minimum1.5563
Maximum5.7718
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2024-02-01T23:07:40.009189image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1.5563
5-th percentile1.68642
Q12.1074
median2.7071
Q33.8322
95-th percentile5.4042
Maximum5.7718
Range4.2155
Interquartile range (IQR)1.7248

Descriptive statistics

Standard deviation1.2012461
Coefficient of variation (CV)0.39367543
Kurtosis-0.61166104
Mean3.0513617
Median Absolute Deviation (MAD)0.736
Skewness0.76391318
Sum845.2272
Variance1.4429923
MonotonicityNot monotonic
2024-02-01T23:07:40.200320image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.744 2
 
0.7%
1.9711 1
 
0.4%
3.5951 1
 
0.4%
3.1811 1
 
0.4%
3.2462 1
 
0.4%
3.2403 1
 
0.4%
3.239 1
 
0.4%
3.2098 1
 
0.4%
3.4508 1
 
0.4%
3.2591 1
 
0.4%
Other values (266) 266
95.7%
ValueCountFrequency (%)
1.5563 1
0.4%
1.5611 1
0.4%
1.5666 1
0.4%
1.5733 1
0.4%
1.5799 1
0.4%
1.5872 1
0.4%
1.5919 1
0.4%
1.6287 1
0.4%
1.6294 1
0.4%
1.6344 1
0.4%
ValueCountFrequency (%)
5.7718 1
0.4%
5.6973 1
0.4%
5.643 1
0.4%
5.6407 1
0.4%
5.6199 1
0.4%
5.5805 1
0.4%
5.5302 1
0.4%
5.476 1
0.4%
5.4759 1
0.4%
5.4713 1
0.4%

Interactions

2024-02-01T23:07:31.122588image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:06:58.248129image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:01.089638image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:03.334630image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:05.834188image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:08.606371image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:11.191180image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:13.619685image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:15.784892image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:18.199430image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:20.805763image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:22.805141image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:24.847677image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:26.823374image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:29.293907image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:31.241269image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:06:58.589216image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:01.256192image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:03.467297image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:06.005821image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:08.769934image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:11.374689image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:13.756321image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:15.967403image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:18.383935image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:20.935401image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:22.941777image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:24.992287image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:26.968985image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:29.416366image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:31.350443image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:06:58.718869image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:01.398811image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:03.599920image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:06.185341image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:08.924519image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:11.563184image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:13.885972image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:16.222720image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:18.774889image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:21.069091image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:23.072425image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:25.135905image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:27.119582image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:29.543682image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:31.465107image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:06:58.853509image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:01.539456image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:03.724608image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:06.374832image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:09.087592image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:11.726746image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:14.024601image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:16.403237image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:18.924374image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:21.201263image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:23.209088image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:25.274532image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:27.280152image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:29.662153image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:31.593763image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:06:59.054178image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:01.711973image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:03.869199image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:06.543382image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:09.294235image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:11.889322image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:14.170212image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:16.557823image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:19.100903image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:21.343904image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:23.353673image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:25.414158image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:27.443713image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:29.784965image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:31.709485image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:06:59.203779image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:01.860579image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:04.033760image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:06.706944image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:09.469767image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:12.066843image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:14.315824image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:16.754297image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:19.273951image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:21.482512image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:23.492302image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:25.544809image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:27.582346image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:29.905924image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:31.821588image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:06:59.352381image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:02.007183image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:04.191337image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:06.854549image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:09.661251image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:12.198514image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:14.447469image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:16.912873image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:19.450478image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:21.608175image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:23.623950image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:25.669544image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:27.712996image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:30.047276image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:31.944258image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:06:59.497391image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:02.162767image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:04.386814image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:07.008137image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:09.841769image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:12.334126image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:14.583107image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:17.072444image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:19.590891image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:21.738826image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:23.762578image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:25.799221image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:27.846847image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:30.168806image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:32.070919image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:06:59.632031image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:02.324335image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:04.579303image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:07.170702image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:09.999347image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:12.486718image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:14.716749image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:17.206087image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:19.741488image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:21.874531image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:23.901207image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:25.924860image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:27.983287image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:30.297720image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:32.195638image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:06:59.771658image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:02.483906image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:04.758057image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:07.338255image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:10.182856image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:12.741039image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:14.864378image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:17.367655image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:19.882083image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:22.032111image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:24.045821image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:26.059519image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:28.342295image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:30.423412image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:32.321301image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:06:59.895325image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:02.620564image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:04.907657image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:07.476883image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:10.334450image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:12.922552image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:14.998994image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:17.501320image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:20.015725image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:22.163757image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:24.180459image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:26.179180image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:28.473944image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:30.533231image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:32.444971image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:00.155630image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:02.764184image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:05.082203image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:07.640452image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:10.482054image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:13.078135image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:15.165549image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:17.654886image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:20.157347image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:22.300392image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:24.323078image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:26.311825image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:28.662441image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:30.656286image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:32.554948image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:00.544590image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:02.928716image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:05.361453image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:08.016442image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:10.614701image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:13.212775image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:15.304178image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:17.791520image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:20.304078image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:22.421099image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:24.455724image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:26.427735image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:28.817183image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:30.765826image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:32.678257image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:00.738578image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:03.090284image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:05.543966image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:08.167037image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:10.767291image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:13.366363image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:15.476717image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:17.939126image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:20.470632image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:22.558699image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:24.602332image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:26.584340image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:28.992723image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:30.887470image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:32.779956image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:00.906129image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:03.214950image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:05.687582image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:08.391944image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:10.941846image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:13.499009image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:15.614349image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:18.067781image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:20.680071image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:22.692470image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:24.727995image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:26.704705image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:29.145306image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T23:07:31.013549image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Correlations

2024-02-01T23:07:40.553680image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
CNYCoffeeEURIron OreMeat indexSoybeansSugarUSDbud_group_personal_spent_valuebud_type_mandatory_spent_valueeco_GDP_R$_12_monthseco_net_debt_R$eco_net_debt_R$_federal_govtexp_DIC_yexp_trade_balance_y
CNY1.0000.2490.9320.2290.5290.2240.1150.9550.7560.7560.7550.7490.7320.5800.531
Coffee0.2491.0000.1670.8240.7920.8240.8710.0550.6950.6950.6940.6980.7110.5550.262
EUR0.9320.1671.0000.1500.4340.1650.0270.9300.7040.7040.7020.6990.6860.4600.673
Iron Ore0.2290.8240.1501.0000.7560.8120.8210.0120.6870.6870.6850.6860.6900.5640.236
Meat index0.5290.7920.4340.7561.0000.7450.6690.3370.8280.8280.8270.8290.8340.7370.368
Soybeans0.2240.8240.1650.8120.7451.0000.771-0.0010.6320.6320.6310.6320.6320.5220.221
Sugar0.1150.8710.0270.8210.6690.7711.000-0.0800.6000.6000.5970.5990.6070.5110.235
USD0.9550.0550.9300.0120.337-0.001-0.0801.0000.6130.6130.6100.6060.5920.4220.572
bud_group_personal_spent_value0.7560.6950.7040.6870.8280.6320.6000.6131.0001.0001.0000.9990.9950.8530.639
bud_type_mandatory_spent_value0.7560.6950.7040.6870.8280.6320.6000.6131.0001.0001.0000.9990.9950.8530.639
eco_GDP_R$_12_months0.7550.6940.7020.6850.8270.6310.5970.6101.0001.0001.0000.9990.9950.8570.635
eco_net_debt_R$0.7490.6980.6990.6860.8290.6320.5990.6060.9990.9990.9991.0000.9970.8560.636
eco_net_debt_R$_federal_govt0.7320.7110.6860.6900.8340.6320.6070.5920.9950.9950.9950.9971.0000.8520.644
exp_DIC_y0.5800.5550.4600.5640.7370.5220.5110.4220.8530.8530.8570.8560.8521.0000.384
exp_trade_balance_y0.5310.2620.6730.2360.3680.2210.2350.5720.6390.6390.6350.6360.6440.3841.000

Missing values

2024-02-01T23:07:32.944515image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-02-01T23:07:33.279680image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Timeeco_net_debt_R$eco_net_debt_R$_federal_govteco_GDP_R$_12_monthsCoffeeIron OreMeat indexSoybeansSugarbud_group_personal_spent_valuebud_type_mandatory_spent_valueexp_DIC_yexp_trade_balance_yCNYEURUSD
02001-01541333.74337611.991209046.140.07117622.18041165.93872948.36489157.6671716.267857e+101.363717e+1124.000.500.2385881.843701.9711
12001-02550253.48346552.501218911.041.06171022.18041167.67504646.16763854.2375856.267857e+101.363717e+1124.00-0.270.2475511.891532.0452
22001-03561959.20355037.501234635.040.68964622.18041174.21655445.31913151.0388216.267857e+101.363717e+1123.45-1.000.2616591.901652.1616
32001-04565464.63360694.521250830.740.28175422.18041174.33200243.72976447.7289436.267857e+101.363717e+1122.70-1.250.2644641.941642.1847
42001-05581727.09374879.991263306.042.49957922.18041177.67519045.17982452.1689866.267857e+101.363717e+1121.00-1.500.2856992.001342.3600
52001-06586060.21378378.891265570.038.96862622.18041179.21201746.93382750.9820746.267857e+101.363717e+1120.00-1.320.2790391.963602.3049
62001-07606367.82395250.931272918.635.97495722.18041179.88395551.46130849.9452056.267857e+101.363717e+1118.00-1.000.2943302.132832.4313
72001-08622953.86408431.081280805.436.21295822.18041180.99869650.38484446.9134066.267857e+101.363717e+1118.80-0.800.3089092.330612.5517
82001-09635685.65418457.601289198.635.56335822.18041179.29576547.51638443.2539546.267857e+101.363717e+1118.000.000.3233922.435842.6713
92001-10646099.14418907.031298386.534.40419422.18041174.70832344.28699240.3749666.267857e+101.363717e+1118.601.200.3277262.441672.7071
Timeeco_net_debt_R$eco_net_debt_R$_federal_govteco_GDP_R$_12_monthsCoffeeIron OreMeat indexSoybeansSugarbud_group_personal_spent_valuebud_type_mandatory_spent_valueexp_DIC_yexp_trade_balance_yCNYEURUSD
2682023-055922818.045006344.8410476147.9134.351395178.470757135.034451140.153167143.0155353.503768e+112.214626e+1280.0059.80000.71675.42875.0959
2692023-065992871.685169640.1210526477.7126.577771193.102605135.688510145.102054137.7599773.529402e+112.238686e+1279.5063.75990.66465.26264.8192
2702023-076067062.705256528.0210569414.8118.099551194.614222135.839551153.047631133.0681993.555037e+112.262745e+1280.0066.50000.66385.22514.7415
2712023-086163293.135315415.0610620107.5113.737167187.156730131.569790140.650878134.6639753.580672e+112.286805e+1280.0073.00000.67815.33534.9219
2722023-096211220.805375619.9710666257.2112.053873205.631656128.305785134.309984147.1682083.606306e+112.310864e+1280.0072.10000.68595.30005.0076
2732023-106253007.525402090.7910732193.6112.275013202.765497127.173451130.113647149.4593333.631941e+112.334924e+1271.7575.15000.69125.34535.0575
2742023-116333747.915473823.0810803176.4120.503662224.109233123.753638136.294350151.6590243.657575e+112.358983e+1262.8078.40000.69165.38564.9355
2752023-12NaNNaNNaN128.363340234.862411121.392080132.748212124.3125733.683210e+112.383042e+1259.0081.30000.68155.35164.8413
2762024-01NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN68.4278.45000.69115.38054.9535
2772024-02NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN